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. 2013 Oct 9:3:2898.
doi: 10.1038/srep02898.

Soft truncation thresholding for gene set analysis of RNA-seq data: application to a vaccine study

Brooke L Fridley  1 , Gregory D Jenkins, Diane E Grill, Richard B Kennedy, Gregory A Poland, Ann L Oberg
Affiliations

Soft truncation thresholding for gene set analysis of RNA-seq data: application to a vaccine study

Brooke L Fridley et al. Sci Rep. .

Abstract

Gene set analysis (GSA) has been used for analysis of microarray data to aid the interpretation and to increase statistical power. With the advent of next-generation sequencing, the use of GSA is even more relevant, as studies are often conducted on a small number of samples. We propose the use of soft truncation thresholding and the Gamma Method (GM) to determine significant gene set (GS), where a generalized linear model is used to assess per-gene significance. The approach was compared to other methods using an extensive simulation study and RNA-seq data from smallpox vaccine study. The GM was found to outperform other proposed methods. Application of the GM to the smallpox vaccine study found the GSs to be moderately associated with response, including focal adhesion (p = 0.04) and extracellular matrix receptor interaction (p = 0.05). The application of GSA to RNA-seq data will provide new insights into the genomic basis of complex traits.

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Conflict of interest statement

GAP is the chair of a Safety Evaluation Committee for novel investigational vaccine trials being conducted by Merck Research Laboratories. GAP offers consultative advice on vaccine development to Merck & Co. Inc., CSL Biotherapies, Avianax, Sanofi Pasteur, Dynavax, Novartis Vaccines and Therapeutics, and PAXVAX Inc. These activities have been reviewed by the Mayo Clinic Conflict of Interest Review Board and are conducted in compliance with Mayo Clinic Conflict of Interest policies. All other authors have no disclosures to disclose.

Figures

Figure 1
Figure 1. Power comparison between the Gamma Method with STT = 0.15 and 0.05 (GM.15, GM.05), Fisher's Method (FM), Global model with random effects (GMRE), Principal components analysis with 80% of components that explained the variability included in the model (PCA.80), Kolmogorov-Smirnov test (KS).
Figure 2
Figure 2. Power comparison between the Gamma Method with various STT values.
STT values ranged from 0.20 to 0.01.
Figure 3
Figure 3. Dendrogram of top 25 GS associated with response to smallpox vaccine to visualize relationship and overlap between gene sets.
GSs containing a large set of genes in common would be clustered close together while GSs with no genes in common would not be clustered together.
See this image and copyright information in PMC

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